Radiomics analysis derived from LGE-MRI predict sudden cardiac death in participants with hypertrophic cardiomyopathy

Jie Wang, Laura Bravo, Jinquan Zhang, Wen Liu, Ke Wan, Jiayu Sun, Yanjie Zhu, Yuchi Han, Georgios V Gkoutos, Yucheng Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: To identify significant radiomics features derived from late gadolinium enhancement (LGE) images in participants with hypertrophic cardiomyopathy (HCM) and assess their prognostic value in predicting sudden cardiac death (SCD) endpoint.

Method: The 157 radiomic features of 379 sequential participants with HCM who underwent cardiovascular magnetic resonance imaging (MRI) were extracted. CoxNet (Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net) and Random Forest models were applied to optimize feature selection for the SCD risk prediction and cross-validation was performed.

Results: During a median follow-up of 29 months (interquartile range, 20-42 months), 27 participants with HCM experienced SCD events. Cox analysis revealed that two selected features, local binary patterns (LBP) (19) (hazard ratio (HR), 1.028, 95% CI: 1.032-1.134; P = 0.001) and Moment (1) (HR, 1.212, 95%CI: 1.032-1.423; P = 0.02) provided significant prognostic value to predict the SCD endpoints after adjustment for the clinical risk predictors and late gadolinium enhancement. Furthermore, the univariately significant risk predictor was improved by the addition of the selected radiomics features, LBP (19) and Moment (1), to predict SCD events (P < 0.05).

Conclusion: The radiomics features of LBP (19) and Moment (1) extracted from LGE images, reflecting scar heterogeneity, have independent prognostic value in identifying high SCD risk patients with HCM.

Original languageEnglish
Article number766287
Number of pages11
JournalFrontiers in cardiovascular medicine
Volume8
DOIs
Publication statusPublished - 10 Dec 2021

Keywords

  • hypertrophic cardiomyopathy
  • machine learning
  • sudden cardiac death
  • late gadolinium enhancement
  • radiomics

Fingerprint

Dive into the research topics of 'Radiomics analysis derived from LGE-MRI predict sudden cardiac death in participants with hypertrophic cardiomyopathy'. Together they form a unique fingerprint.

Cite this